Gender: Male 
Age: 35-44
Nationality: United States
Ethnicity: White / Caucasian 
Marital status: Married 
First language: English 
Religion: Nothing 
Education: Undergraduate degree
Mother's education: Undergraduate degree
Father's education: Graduate degree
Self-assessed socioeconomic status (1-10): 5
Home: Rent 
Employment: Employed full time
Occupation: Clerical support workers (those who perform clerical duties such as organizing, storing, computing and retrieving information)
Mother's occupation: Service and sales workers (whose who provide personal and protective services related to travel, housekeeping, catering, personal care, protection against fire and unlawful acts)
Father's occupation: Manager or business owner (those who plan, direct, coordinate and evaluate the overall activities of enterprises, governments and other organizations)
Hobbies: Listen to hiphop and rap;Exercise;Watch sports;Go to museums and galleries 
Devices used daily: Laptop;Smartwatch 
NLP tools known: Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Text-to-Speech (i.e, computers being able to read out loud written language);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Dialog technology (e.g. chatbots that communicate with you such as chatGPT) 
NLP tools used: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Speech-to-Text (i.e. a computer transcribing your spoken language to written language) 
NLP tools they would find useful: Text-to-Speech (i.e, computers being able to read out loud written language);Speech-to-Text (i.e. a computer transcribing your spoken language to written language) 
Frequency of LLM use: Sometimes
LLMs used: ChatGPT;GitHub Copilot;Google Gemini 
LLM use cases: Coding;Translation;Brainstorming;Writing (e.g., composing emails);Proofreading/Editing (e.g., grammar, spelling, structure improvements);Solving math or logical problems;Analyze data;Completing assignments;Collecting references;Summarizing long texts 
Contexts of LLM use: Personal;Learning 
Prompt 1: how does prolific pricing work.
Prompt 2: is this sentence correct? 'Thank you! I'll be expecting.'
Prompt 3: between Burger king, chick fil A, McDonalds, Subway, Taco bell and Wendy's, rate the prices.
Prompt 4: a word relating to cather.
Prompt 5: Temperature of oven for moisture content.
Prompt 6: how to calculate credit hours.
Prompt 7: another text editor to replace vim.
Prompt 8: give me questions on z and t distribution.
Prompt 9: what are the angles inside of an X.
Prompt 10: what is unity path.